Banana.dev

Banana.dev is designed to host virtually any machine learning model that can be containerized. While we specialize in large language models and generative AI like Stable Diffusion, our serverless GPU infrastructure supports any framework including PyTorch, TensorFlow, and JAX for high-performance inference.

Our platform utilizes a serverless architecture that automatically scales your GPU resources based on incoming request volume. When traffic spikes, we spin up additional replicas instantly; when traffic drops, we scale down to zero so you never pay for idle compute time.

We optimize cold starts through aggressive container layering and model caching on our GPU nodes. While specific times vary by model size, most optimized containers resume execution in under a few seconds, ensuring your end-users experience minimal delays even after periods of inactivity.

Migration is straightforward: you simply wrap your model code in our 'Potassium' framework, define your dependencies in a Dockerfile, and deploy via our CLI. Our documentation provides templates for popular models to help you get your first endpoint live in under ten minutes.

No, Banana.dev is a fully managed abstraction layer. We handle all the underlying orchestration, GPU provisioning, and networking. You only need to provide the model code and container specifications; our platform takes care of the operational complexity.

Absolutely. Banana.dev provides a robust CLI and GitHub integration that allows for automated deployments. Every time you push code to your repository, our system can automatically build your Docker image and deploy the updated model to our serverless fleet.

We offer a comprehensive REST API that allows you to programmatically manage your deployments, check model status, and retrieve usage statistics. This makes it easy to build custom internal dashboards or automate scaling logic within your own application stack.

Banana.dev charges based on the exact number of seconds your model is actively running on a GPU. There are no monthly platform fees or minimum commitments, allowing startups to scale from prototype to production while only paying for the actual inference time consumed.

Yes, pricing is tiered based on the specific GPU hardware required for your workload, such as NVIDIA A100s or T4s. You can select the hardware profile that best fits your model's VRAM requirements and performance needs directly within your configuration file.

We treat your model weights and code as highly confidential. All data is encrypted at rest and in transit, and each model run is isolated within its own secure container environment to prevent cross-tenant data leakage or unauthorized access to your intellectual property.

Our infrastructure is primarily hosted in high-availability data centers across the United States. While we automatically route traffic for optimal performance, enterprise customers can contact support to discuss specific regional requirements for data residency and compliance needs.

We provide extensive documentation, community Discord access, and email support for all users. Enterprise tier customers receive a dedicated Slack channel and priority support with guaranteed response times to assist with complex architectural challenges or production issues.